Face detection and object detection

Part 1 - Face Detection

In this project, we aim to construct a face detection model. We used a method haar to extract features. After that, by applying extracted features to cascade method, we were able to dectect people's faces and also count the number of faces through pictures as well as webcam. Pre-trained cascade was used from OpenCV.

We suggest clone or download the whole repository to reproduce the result. You can reproduce them all from this notebook. Besides, you are encouraged to add your own test images simply by putting all the images you want to test into the data/test_data/cascade or data/test_data/tensorflow directory.

You can hit the button below to show the full codes and hit again to collapse all cells.

Part 1.1 - Face Detection without Rotation on Image

We started off the project with face detection on an image.

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In [14]:
import os
import sys
import numpy as np
import count_face as cf
import counting_faces_image as cfi
import counting_faces_webcam as cfw
import tensorflowFn as tf_image
import tensorflowFnVideo as tf_video
import ipywidgets as widgets
from pathlib import Path
from matplotlib import pyplot as plt
from IPython.display import HTML
from IPython.display import display
In [2]:
# Apply face detection
cf.counting_face()

Part 1.2 - Face Detection with Rotation

After implementation of Part 1, we realized the OpenCV front face cascade could not detect rotated face. Hence, we made some adjustments and declared additional functions to allow our model to analyze images from different rotation angle.

In [18]:
'''

You can put your own images into the directory: '../data/test_image/cascade'

'''


cfi.face_dectect_image('../data/test_image/cascade/')

Part 1.3 - Real Time Face Detection with WebCam

After we improved our model, we wanted to further develop our model. Therefore, in this part, we implemented real time face detection using WebCam. You can click on the button below to release the cam. Press "Q" to quit the cam.

In [17]:
'''




Press Q to close the camera 




'''


cfw.face_dectect_webcam()

Part 2 - Object Detection API using Tensorflow

After we constructed our model in Part 1, we realized there exist some limitations in cascade model. Cascade model tends to have lower accuracy in side faces or partially showed faces. Also, cascade cannot detect highly rotated faces. To overcome such limitations, a popular and powerful approach is the use of tensorflow. In this section, we implement object detection with a pretrained model, Tensorflow Object Detection API. This model requires intallation of tensorflow. Further instruction of the installation can be referred to https://github.com/tensorflow/models/tree/master/research/object_detection. This model can detect and categorize object, including person, bottle, cellphone, etc. However, cascada model would result better if only faces are showed on an image while this API model would result better if more parts of human body are showed.

Part 2.1 - Object Detection API with Tensorflow on Image

Similar to Part 1.1, we started off with object detection using image. We can see now the model can not only detect human faces but the entire human figure. Moreover, this more advanced model allows us to detect objects other than human, for example, dogs, cats, bottles, cell phones, clocks and so on.

In [17]:
'''

You can put your own images into the directory: '../data/test_image/tensorflow'


'''

tf_image.objectDetection('../../data/test_image/tensorflow')

Part 2.2 - Real Time Object Detection API with Tensorflow using WebCam

Part 2.1 resulted in highly accurate result detecting most of the objects into their corresponding categories. We further improved the model by implementing the model using WebCam as an input. The result was again very accurate. You can click on the button below to release the cam. Press "Q" to quit the cam.

In [3]:
'''




Press Q to close the camera 




'''


tf_video.objectDetectionCap()